Bayesian methods are useful in modern data analytics as they may fuse sparse information with models of different fidelity levels. A Bayesian network is a representation of the probabilistic relationship among variables. The probability distribution specifies the strength of the relationship between variables. The Bayesian rule provides a standard set of procedures and formula to determine this relationship. A standard procedure used in solving the Bayesian rule is numerical integration. However, a problem with conventional Bayesian methods that use numerical integration is that numerical integration requires a large amount of computation, which can become computationally expensive. Conventionally, some solutions to the slow computations are to either simplify the models so that they can run fast enough, or to derive approximate solutions. The problem with both of these conventional approaches (model simplification and approximation) is that they lose accuracy. Another alternative is to employ heuristic approaches, which may be problem-specific and therefore may be very difficult to generalize.
Therefore, it is desirable to provide a system and method that more efficiently solves the Bayesian rule.